Canine Handler Operations Positioning System

ABSTRACT

The Canine Handler Operations Positioning System (the Inventors) taught by the present invention consists of one or more dog-worn sensor, one or more handler&#39;s shoe-worn sensor, and algorithms for maintaining localization of units of canines and handlers traveling in GPS and GPS-denied areas. The present invention adapts the localization algorithms from the human-based system to dogs, increase performance, reduce SWAP, and further refine the system based on user feedback. The human worn system is modified for the human handler for maximum operational practicality in regard to batteries, size, and interoperability to a radio. The Canine Handler Operations Positioning System (the Inventors) focuses on developing the dog-worn positioning system, modifying the handler&#39;s positioning sensor if needed, and integrating the system with an OCU. The complete the Inventors system would provide a positioning solution for both the dog(s) and handler(s).

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. patent application Ser. No.61/679,355, entitled “System and Method for Urban Mapping andPositioning”, filed on 3 Aug. 2012. The benefit under 35 USC §119(e) ofthe United States provisional application is hereby claimed, and theaforementioned application is hereby incorporated herein by reference.

FEDERALLY SPONSORED RESEARCH

Not Applicable

SEQUENCE LISTING OR PROGRAM

Not Applicable

TECHNICAL FIELD OF THE INVENTION

This invention relates to a canine handler operations positioning systemfor use in military applications. In particular, the invention relatesto the use of a canine handler operations positioning system thatconsists of one or more dog-worn sensors, one or more handler'sshoe-worn sensors, and algorithms for maintaining localization of unitsof canines and handlers traveling in GPS and GPS-denied areas.

BACKGROUND OF THE INVENTION

Dogs are used by Special Operations Forces for a variety ofIntelligence, Surveillance, and Reconnaissance missions. In IEDmissions, a dog may be sent into a tunnel, building, orhuman-inaccessible space. To make matters more difficult, the task maybe performed in GPS-denied areas. When a canine is sent off on its own,the operator has limited knowledge of the dog's precise path, location,or status (e.g., the dog has pause for significant periods of time). Inaddition to IED/EOD operations, other SOCOM missions make extensive useof dogs; pursuit of a suspect, rescue missions, guarding activities, andsearching for other objects or persons-of-interest. Accuratelocalization is beneficial in all of these missions.

The United States Military has developed special equipment for theirworking dogs, including a special camera system that is incorporatedinto vests worn by combat dogs. The dogs take over a year and $60,000 totrain. Dogs can out-smell any human and most mechanical sensing deviceswhile possessing excellent mobility capabilities. A major limitingfactor is the canine's lack of communication. Mission efficacy andsuccess will increase by knowing the location of the dog in real-time.When a canine discovers an object of interest (e.g., contraband orwounded personnel), having precise knowledge of the dog's position makesachieving follow-on objectives quicker, thus saving lives. Additionally,the ability to find a wounded/trapped dog will save a valued asset.

Therefore, what is needed is a canine handler operations positioningsystem that consists of one or more dog-worn sensors, one or morehandler's shoe-worn sensors, and algorithms for maintaining localizationof units of canines and handlers traveling in GPS and GPS-denied areas.

Definitions

GPS stands for Global Positioning System, is a radio navigation systemthat allows land, sea, and airborne users to determine their exactlocation, velocity, and time 24 hours a day, in all weather conditions,anywhere in the world.

GPS-denied areas are defined as land, sea, and airborne locations thatare unable to receive a radio signal for GPS navigation.

AUGV is defined as an Autonomous Unmanned Ground Vehicle.

DTED (or Digital Terrain Elevation Data) is a standard of digitaldatasets which consists of a matrix of terrain elevation values.

LIDAR (Light Detection And Ranging, also LADAR) is an optical remotesensing technology that can measure the distance to, or other propertiesof, targets by illuminating the target with laser light and analyzingthe backscattered light. LIDAR technology has applications in geomatics,archaeology, geography, geology, geomorphology, seismology, forestry,remote sensing, atmospheric physics, airborne laser swath mapping(ALSM), laser altimetry, and contour mapping.

EO image is an image created or provided from an earth observatory.

Soldiers are defined as military wearers of the present invention.

Handlers are military soldiers or non-military wearers of the presentinvention. Handlers and Soldiers can be used interchangeably in thisdocument, as they are simply the human wearers of the present invention.

SUMMARY OF THE INVENTION

The Canine Handler Operations Positioning System (the Inventors) taughtby the present invention consists of one or more dog-worn sensor, one ormore handler's shoe-worn sensor, and algorithms for maintaininglocalization of units of canines and handlers traveling in GPS andGPS-denied areas. The present invention adapts the localizationalgorithms from the human-based system to dogs, increase performance,reduce SWAP, and further refine the system based on user feedback. Thehuman worn system is modified for the human handler for maximumoperational practicality in regard to batteries, size, andinteroperability to a radio. The Canine Handler Operations PositioningSystem (the Inventors) focuses on developing the dog-worn positioningsystem, modifying the handler's positioning sensor if needed, andintegrating the system with an OCU. The complete the Inventors systemwould provide a positioning solution for both the dog(s) and handler(s).

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate the present invention and, togetherwith the description, further serve to explain the principles of theinvention and to enable a person skilled in the pertinent art to makeand use the invention.

FIG. 1 is a conceptual example of the network proposed for increasepositioning accuracy for robots, personnel, and canines; and

FIG. 2 is a visualization of a real matrix used to solve a springproblem.

DESCRIPTION OF THE INVENTION

In the following detailed description of the invention of exemplaryembodiments of the invention, reference is made to the accompanyingdrawings (where like numbers represent like elements), which form a parthereof, and in which is shown by way of illustration specific exemplaryembodiments in which the invention may be practiced. These embodimentsare described in sufficient detail to enable those skilled in the art topractice the invention, but other embodiments may be utilized andlogical, mechanical, electrical, and other changes may be made withoutdeparting from the scope of the present invention. The followingdetailed description is, therefore, not to be taken in a limiting sense,and the scope of the present invention is defined only by the appendedclaims.

In the following description, numerous specific details are set forth toprovide a thorough understanding of the invention. However, it isunderstood that the invention may be practiced without these specificdetails. In other instances, well-known structures and techniques knownto one of ordinary skill in the art have not been shown in detail inorder not to obscure the invention. Referring to the figures, it ispossible to see the various major elements constituting the apparatus ofthe present invention.

UMAPS is a multifaceted system that can be robot-mounted, human-worn, orcanine carried. UMAPS produces real-time, 3D mapping and localizationfor the user as they move throughout a GPS-denied environment (e.g.buildings, caves, or tunnels). An Operator Control Unit (OCU) displaysinformation collected by UMAPS; 2D floorplans; 3D textured-enrichedsurfaces of the structure's interior; and the location of the userswithin that structure. UMAPS has an open architecture that allows it tofunction with any OCU. UMAPS has three distinct subsystems: obstaclemaps for robot mobility, mapping, and positioning as shown in FIG. 1.

These subsystems can be combined to provide products that addressspecific military needs. For example, one capability that can beproduced is UMAPS-Positioning (UMAPS-P), which provides GPS-deniedposition estimation for human users. The applications of such technologyinclude localization within a building and exploration of tunnels with aquick determination of the position of each squad member. Anothercapability is for a human-worn mapping component of UMAPS that canprovide increased situational awareness for command and control,identify unexplored regions of a building, and provide advancedintelligence for warfighters. A third capability allows mapping to begenerated from a robotic platform, providing critical 3-D surveyinformation of remote, enclosed or dangerous spaces without compromisinghuman presence or safety. A fourth capability allows the determinationof canine unit positioning relative to its mission objective.

As a result of testing, the UMAPS positioning subsystem improvementsover the initial concept included integrating GPS with the inertialnavigation system for better global accuracy.

In other embodiments the mapping prototype was adapted for mounting on abackpack and a robotic platform.

The information from multiple localization methods will be fused bycreating a “spring” network among different updates, using the springsto “pull” the navigation solutions to a more accurate location. Thestrength of each spring will be determined by the confidence of theupdate. This type of architecture was originally developed by RR andused on the UMAPS program to fuse the relative localization solution ofmultiple INS systems strapped to dismounted soldiers. As the soldiersexplored the GPS-denied area, their INS navigation solution was sent toan OCU. If the soldiers met at a “rendezvous” location, this was sent tothe OCU which then created an infinitely tight spring between theirnavigation solutions at that time, essentially locking them into thesame place. Looser springs existed along each of their relativesolutions in order to keep the general shape of the path that they took.Springs also existed to absolute positions, which were either surveyedpoints that the soldiers tagged (infinitely tight springs) or GPSupdates when available (somewhat looser springs).

In an alternative embodiment, this same framework will be used by theproposed system, but instead of tagging “rendezvous” points to fusemultiple relative solutions, the system will use absolute position“springs” to pull the overall solution toward the updates.

The UMPAS system developed by the inventors is currently capable ofmaintaining localization in GPS-denied areas with accuracies better than1% of distance traveled without calibrating for a particular person.This capability is achieved by performing zero velocity updates at eachfoot-fall, in turn eliminating the accelerometer errors. Through asystem of clever synchronization between multiple localization units andgroup filtering, the system is capable of maintaining very accuraterelative positions between separate units. The same advantages can beextended for canines The handler and the canine will synchronize theirunits (telling the group filtering systems that they are in closeproximity); this action eliminates errors in the relative position ofthe pair. Further synchronizations will continue adjusting heading andposition of the team.

The present invention extends and refines the positioning technologydeveloped under the UMAPS programs specifically for the dog localizationcase. This work includes system and manufacturing engineering, systemintegration, algorithm refinement, and further refinement, andidentification of suitable sensors. The specific type and size of thedog will be determined with help from the sponsor.

The present invention adapts the localization algorithms from thehuman-based system to dogs, increase performance, reduce SWAP, andfurther refine the system based on user feedback. The human worn systemis modified for the human handler for maximum operational practicalityin regard to batteries, size, and interoperability to a radio.

The Canine Handler Operations Positioning System (the Inventors) taughtby the present invention consists of one or more dog-worn sensor, one ormore handler's shoe-worn sensor, and algorithms for maintaininglocalization of units of canines and handlers traveling in GPS andGPS-denied areas. The handler and the canine will synchronize theirunits (telling the group filtering systems that they are in closeproximity); this action eliminates errors in the relative position ofthe pair. Further synchronizations will continue adjusting heading andposition of the team.

The Canine Handler Operations Positioning System (the Inventors) focuseson developing the dog-worn positioning system, modifying the handler'spositioning sensor if needed, and integrating the system with an OCU.The complete the Inventors system would provide a positioning solutionfor both the dog(s) and handler(s).

In order to have groups of autonomous (or semi-autonomous) mobilitysystems capable of providing distributed and networked Intelligence,Surveillance, and Reconnaissance (ISR), there are three main componentsneeded: (1) Accurate relative localization (know where each platform isin relation to other platforms); (2) a variety of situation awarenesstools to make the operational information collected from the platformsensors useful to the team members; and (3) when human/robot teams areworking and moving together, an ability to localize and interfacebetween humans and robots (to know where each other is located). Thiseffort is for improving the localization for collaborative ISRautonomous unmanned systems and humans, (with/without GPS), customizingand hardening the autonomous capabilities for SOCOM mission sets, andextensive testing with appropriate unmanned vehicles and human andcanine team members in military relevant SOF mission scenarios.

The technology for an autonomous or semi-autonomous mobility system hasreached a maturity that now requires customization and testing in a SOFmission-set environment. As stated in the abstract, there are threeareas that must be addressed to meet these needs: (1) Accurate platformlocalization, (2) Situation Awareness (SA) tools and (3) teampositioning.

The information from multiple localization methods will be fused bycreating a “spring” network among different updates, using the springsto “pull” the navigation solutions to a more accurate location. Thestrength of each spring will be determined by the confidence of theupdate. This type of architecture was originally developed by RR andused on the UMAPS program to fuse the relative localization solution ofmultiple INS systems strapped to dismounted soldiers. As the soldiersexplored the GPS-denied area, their INS navigation solution was sent toan OCU. If the soldiers met at a “rendezvous” location, this was sent tothe OCU which then created an infinitely tight spring between theirnavigation solutions at that time, essentially locking them into thesame place. Looser springs existed along each of their relativesolutions in order to keep the general shape of the path that they took.Springs also existed to absolute positions, which were either surveyedpoints that the soldiers tagged (infinitely tight springs) or GPSupdates when available (somewhat looser springs). This same frameworkwill be used by the Inventors, but instead of tagging “rendezvous”points to fuse multiple relative solutions, the system will use absoluteposition “springs” to pull the overall solution toward the updates.

FIG. 1 shows a conceptual example of how the spring network will work.The first path 101 and second paths 102 are two separate runs by theAUGV. The AUGV will compute its relative localization solution using itsinertial sensors and available odometry. At Spring A 103 in the figureduring the first run 101, the AUGV may get a poor GPS update, so itinserts a weak spring into the network, slightly pulling its path towardthat solution. At the end of the run, it inserts Spring B 104 byregistering some local sensor data to aerial imagery. The registrationalgorithm reports high confidence, so the spring is strong. Since it hasa confident absolute position solution, it geo-tags some local sensordata and stores it for a future run. During the next, second run 102,the AUGV is able to register its position to some DTED data because of aunique change in elevation and inserts Spring C 103. Nearby, Spring D104 is inserted by registering the local LIDAR sensor data to someaerial LIDAR in a stored database. At the end of the second run 102, itregisters some local features from an EO image to data from the previousrun and inserts a strong Spring E 105 that pulls it to the sameendpoint. These springs allow each absolute localization method toprovide a weight or confidence level to affect how much the overallsolution gets corrected. Infinitely strong springs perform a sort of“localization reset.”

The concept of springs as discussed above is solved using least squaresof the form Ax=b, where A characterizes the springs, b quantifies thestrength of the spring, and x are the offsets applied to the navigationsolutions. For non-square matrix A, the naïve approach to solve thisleast squares problem is x=(ÂT A)̂(−1) ÂT b, which is called the normalequation. However, this approach is computationally expensive andnumerically unstable (for some choices of A) because of the requiredmatrix inversion.

In practice, least squares problems are solved with an algorithm such asQR factorization. In its simplest form, QR factorization is atransformation on the matrix A such that A=QR, where Q is orthonormal(i.e., Q̂T Q=I) and R is upper triangular. Substituting thisfactorization into the normal equation above, we find x=(ÂT A)̂(−1) ÂTb=((QR)̂T QR)̂(−1) (QR)̂T b=(R̂T Q̂T QR)̂(−1) R̂T Q̂T b=(R̂T R)̂(−1) R̂T Q̂T b=R̂(−1)Q̂T b. The normal equation simplifies to Rx=Q̂T b, which can beefficiently solved with a single matrix multiply and a linear equationsolver.

Now that the use of QR factorization has been justified, there is now aquestion of how to efficiently solve the QR factorization of a matrix,preferably in a distributed form. One method to solve a QR factorizationis called Householder QR. In Golub and Loan, the Householder QRalgorithm is defined for a general matrix. Without copying the algorithmhere, one key feature to note is that a Householder transformation onrow j affects only rows in A with below diagonal, non-zero entries inthat column. This fact is the key to solving the distributed QRfactorization for this problem.

The matrix A 200 for which we are solving is block lower triangular, asshown in FIG. 2. Since many of the entries below the diagonal 201 arealready zero, much of the Householder transformations can be simplifiedand broken into separate problems. Lu and Barlow define the algorithm todo this factorization. The basis of this work is on analyzing thestructure of the matrix A and how to divide it up into sub-problems.This analysis is performed with column intersection graphs, which linktwo columns that have at least one non-zero entry in both rows. Columnintersection graphs are then transformed into elimination trees,identifying sub-problems that can be computed by different processingunits. For brevity, the entirety of Lu and Barlow's work is notpresented here.

An important component of the present invention is that the relativelocalization of handlers and canines is performed with collectivefiltering techniques. In other words, the relative position of eachhandler and canine is known very accurately (within a couple of meters).ISR is not an independent component of the mission, relative location ofevents with respect to other team members becomes one of the mostimportant aspects of the mission.

An accurate relative localization solution can greatly enhance theperformance of any absolute localization method for several reasons.First, it can reduce the search space of the method when trying toregister local sensors against a priori databases. Second, many of thematching algorithms use a gradient descent method, which will convergeto a correct local minimum faster if seeded with a better startingpoint. Finally, it can be used to filter out erroneous absoluteestimates. If the relative localization solution thinks the handler orcanine has only traveled 20 meters from its last absolute positionupdate, but an absolute localization method reports an update a mileaway, the update is likely to be an error.

Thus, it is appreciated that the optimum dimensional relationships forthe parts of the invention, to include variation in size, materials,shape, form, function, and manner of operation, assembly and use, aredeemed readily apparent and obvious to one of ordinary skill in the art,and all equivalent relationships to those illustrated in the drawingsand described in the above description are intended to be encompassed bythe present invention.

Furthermore, other areas of art may benefit from this method andadjustments to the design are anticipated. Thus, the scope of theinvention should be determined by the appended claims and their legalequivalents, rather than by the examples given.

The embodiments of the invention in which an exclusive property or privilege is claimed are defined as follows:
 1. A method for canine handler operations positioning comprising: a multifaceted electronic computer system that can be human-worn, or canine carried; affixing the electronic computer system to one or more human handlers; affixing the electronic computer system to one or more canines; producing real-time, 3D mapping and localization for one or more wearers as they move throughout a GPS-denied environment; an Operator Control Unit (OCU) displaying information collected 2D floorplans; 3D textured-enriched surfaces of the structure's interior; and the location of the users within that structure; providing an open architecture that allows it to function with any OCU; three distinct subsystems: obstacle maps for robot mobility, mapping, and positioning; combining the subsystems to provide products that address specific military needs the information from multiple localization methods will be fused by creating a “spring” network among different updates; using the springs to “pull” the navigation solutions to a more accurate location; determining the strength of each spring by the confidence of the update.
 2. The method of claim 1, further comprising the step of producing UMAPS-Positioning (UMAPS-P), which provides GPS-denied position estimation for human and canine users.
 3. The method of claim 1, further comprising the step of providing a human-worn mapping component of UMAPS that can provide increased situational awareness for command and control, identify unexplored regions of a building, and provide advanced intelligence for warfighters.
 4. The method of claim 1, further comprising the step of allowing the determination of determining canine unit positioning relative to its mission objective.
 5. The method of claim 1, further comprising the steps of creating a “spring” network among different updates; using the springs to “pull” the navigation solutions to a more accurate location.
 6. The method of claim 5, further comprising the steps of determining the strength of each spring by the confidence of the update; and using absolute position “springs” to pull the overall solution toward the updates.
 7. The method of claim 5, further comprising the steps of: fusing the relative localization solution of multiple INS systems strapped to dismounted handlers; as the handlers and canines explored the GPS-denied area, their INS navigation solution is sent to an OCU; if the handlers and canines met at a “rendezvous” location, this is sent to the OCU which then creates an infinitely tight spring between their navigation solutions at that time, essentially locking them into the same place; looser springs existed along each of their relative solutions in order to keep the general shape of the path that they took; and springs also existed to absolute positions, which were either surveyed points that the handlers tagged as infinitely tight springs or GPS updates when available.
 8. The method of claim 5, wherein instead of tagging “rendezvous” points to fuse multiple relative solutions, using absolute position “springs” to pull the overall solution toward the updates.
 9. The method of claim 1, further comprising the step of performing zero velocity updates at each foot-fall, in turn eliminating the accelerometer errors.
 10. The method of claim 1, further comprising the steps of providing synchronization between multiple localization units and group filtering; and maintaining very accurate relative positions between separate units.
 11. The method of claim 1, further comprising the steps of synchronizing the handler and canine units; telling the group filtering systems that they are in close proximity; collecting one or more further synchronizations; and adjusting heading and position of the team.
 12. The method of claim 1, further comprising one or more dog-worn sensor; one or more handler's shoe-worn sensor; and algorithms for maintaining localization of units of canines and handlers traveling in GPS and GPS-denied areas.
 13. The method of claim 1, providing a positioning solution for both the dog(s) and handler(s).
 14. The method of claim 1, further comprising providing distributed and networked Intelligence, Surveillance, and Reconnaissance (ISR); determining accurate relative localization of each platform in relation to other platforms; providing a variety of situation awareness tools to make the operational information collected from the platform sensors useful to the team members; and when teams are working and moving together, an ability to localize and interface between team members to know where each other is located. 